How to use the tianshou.trainer.onpolicy_trainer function in tianshou

# collector
    train_collector = Collector(
        policy, train_envs, ReplayBuffer(args.buffer_size))
    test_collector = Collector(policy, test_envs)
    # log
    log_path = os.path.join(args.logdir, args.task, 'ppo')
    writer = SummaryWriter(log_path)

    def save_fn(policy):
        torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))

    def stop_fn(x):
        return x >= env.spec.reward_threshold

    # trainer
    result = onpolicy_trainer(
        policy, train_collector, test_collector, args.epoch,
        args.step_per_epoch, args.collect_per_step, args.repeat_per_collect,
        args.test_num, args.batch_size, stop_fn=stop_fn, save_fn=save_fn,
        writer=writer)
    assert stop_fn(result['best_reward'])
    train_collector.close()
    test_collector.close()
    if __name__ == '__main__':
        pprint.pprint(result)
        # Let's watch its performance!
        env = gym.make(args.task)
        collector = Collector(policy, env)
        result = collector.collect(n_episode=1, render=args.render)
        print(f'Final reward: {result["rew"]}, length: {result["len"]}')
        collector.close()

dist = torch.distributions.Categorical
    policy = A2CPolicy(
        actor, critic, optim, dist, args.gamma, vf_coef=args.vf_coef,
        ent_coef=args.ent_coef, max_grad_norm=args.max_grad_norm)
    # collector
    train_collector = Collector(
        policy, train_envs, ReplayBuffer(args.buffer_size))
    test_collector = Collector(policy, test_envs, stat_size=args.test_num)
    # log
    writer = SummaryWriter(args.logdir)

    def stop_fn(x):
        return x >= env.spec.reward_threshold

    # trainer
    result = onpolicy_trainer(
        policy, train_collector, test_collector, args.epoch,
        args.step_per_epoch, args.collect_per_step, args.repeat_per_collect,
        args.test_num, args.batch_size, stop_fn=stop_fn, writer=writer)
    assert stop_fn(result['best_reward'])
    train_collector.close()
    test_collector.close()
    if __name__ == '__main__':
        pprint.pprint(result)
        # Let's watch its performance!
        env = gym.make(args.task)
        collector = Collector(policy, env)
        result = collector.collect(n_episode=1, render=1 / 35)
        print(f'Final reward: {result["rew"]}, length: {result["len"]}')
        collector.close()

net = net.to(args.device)
    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
    dist = torch.distributions.Categorical
    policy = PGPolicy(net, optim, dist, args.gamma)
    # collector
    train_collector = Collector(
        policy, train_envs, ReplayBuffer(args.buffer_size))
    test_collector = Collector(policy, test_envs, stat_size=args.test_num)
    # log
    writer = SummaryWriter(args.logdir)

    def stop_fn(x):
        return x >= env.spec.reward_threshold

    # trainer
    result = onpolicy_trainer(
        policy, train_collector, test_collector, args.epoch,
        args.step_per_epoch, args.collect_per_step, args.repeat_per_collect,
        args.test_num, args.batch_size, stop_fn=stop_fn, writer=writer)
    assert stop_fn(result['best_reward'])
    train_collector.close()
    test_collector.close()
    if __name__ == '__main__':
        pprint.pprint(result)
        # Let's watch its performance!
        env = gym.make(args.task)
        collector = Collector(policy, env)
        result = collector.collect(n_episode=1, render=1 / 35)
        print(f'Final reward: {result["rew"]}, length: {result["len"]}')
        collector.close()

eps_clip=args.eps_clip,
        vf_coef=args.vf_coef,
        ent_coef=args.ent_coef,
        action_range=None)
    # collector
    train_collector = Collector(
        policy, train_envs, ReplayBuffer(args.buffer_size))
    test_collector = Collector(policy, test_envs, stat_size=args.test_num)
    # log
    writer = SummaryWriter(args.logdir)

    def stop_fn(x):
        return x >= env.spec.reward_threshold

    # trainer
    result = onpolicy_trainer(
        policy, train_collector, test_collector, args.epoch,
        args.step_per_epoch, args.collect_per_step, args.repeat_per_collect,
        args.test_num, args.batch_size, stop_fn=stop_fn, writer=writer)
    assert stop_fn(result['best_reward'])
    train_collector.close()
    test_collector.close()
    if __name__ == '__main__':
        pprint.pprint(result)
        # Let's watch its performance!
        env = gym.make(args.task)
        collector = Collector(policy, env)
        result = collector.collect(n_episode=1, render=1 / 35)
        print(f'Final reward: {result["rew"]}, length: {result["len"]}')
        collector.close()

# collector
    train_collector = Collector(
        policy, train_envs, ReplayBuffer(args.buffer_size),
        preprocess_fn=preprocess_fn)
    test_collector = Collector(policy, test_envs, preprocess_fn=preprocess_fn)
    # log
    writer = SummaryWriter(args.logdir + '/' + 'ppo')

    def stop_fn(x):
        if env.env.spec.reward_threshold:
            return x >= env.spec.reward_threshold
        else:
            return False

    # trainer
    result = onpolicy_trainer(
        policy, train_collector, test_collector, args.epoch,
        args.step_per_epoch, args.collect_per_step, args.repeat_per_collect,
        args.test_num, args.batch_size, stop_fn=stop_fn, writer=writer)
    train_collector.close()
    test_collector.close()
    if __name__ == '__main__':
        pprint.pprint(result)
        # Let's watch its performance!
        env = create_atari_environment(args.task)
        collector = Collector(policy, env, preprocess_fn=preprocess_fn)
        result = collector.collect(n_step=2000, render=args.render)
        print(f'Final reward: {result["rew"]}, length: {result["len"]}')
        collector.close()

# collector
    train_collector = Collector(
        policy, train_envs, ReplayBuffer(args.buffer_size),
        preprocess_fn=preprocess_fn)
    test_collector = Collector(policy, test_envs, preprocess_fn=preprocess_fn)
    # log
    writer = SummaryWriter(args.logdir + '/' + 'a2c')

    def stop_fn(x):
        if env.env.spec.reward_threshold:
            return x >= env.spec.reward_threshold
        else:
            return False

    # trainer
    result = onpolicy_trainer(
        policy, train_collector, test_collector, args.epoch,
        args.step_per_epoch, args.collect_per_step, args.repeat_per_collect,
        args.test_num, args.batch_size, stop_fn=stop_fn, writer=writer)
    train_collector.close()
    test_collector.close()
    if __name__ == '__main__':
        pprint.pprint(result)
        # Let's watch its performance!
        env = create_atari_environment(args.task)
        collector = Collector(policy, env, preprocess_fn=preprocess_fn)
        result = collector.collect(n_episode=1, render=args.render)
        print(f'Final reward: {result["rew"]}, length: {result["len"]}')
        collector.close()